{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization which was recently proposed by [3].\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [3] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [3] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[3] Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_val:  (1000, 3, 32, 32)\n",
      "X_train:  (49000, 3, 32, 32)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "y_train:  (49000,)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.iteritems():\n",
    "  print '%s: ' % k, v.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: Forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ 40.10640554  10.16837505   3.17117626]\n",
      "  stds:  [ 27.91343697  30.96269872  36.18267077]\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  mean:  [ -4.31876757e-16   2.26485497e-16   1.66533454e-17]\n",
      "  std:  [ 0.99999999  0.99999999  1.        ]\n",
      "After batch normalization (nontrivial gamma, beta)\n",
      "  means:  [ 11.  12.  13.]\n",
      "  stds:  [ 0.99999999  1.99999999  2.99999999]\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization\n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print 'Before batch normalization:'\n",
    "print '  means: ', a.mean(axis=0)\n",
    "print '  stds: ', a.std(axis=0)\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "print 'After batch normalization (gamma=1, beta=0)'\n",
    "a_norm, _ = batchnorm_forward(a, np.ones(D3), np.zeros(D3), {'mode': 'train'})\n",
    "print '  mean: ', a_norm.mean(axis=0)\n",
    "print '  std: ', a_norm.std(axis=0)\n",
    "\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print 'After batch normalization (nontrivial gamma, beta)'\n",
    "print '  means: ', a_norm.mean(axis=0)\n",
    "print '  stds: ', a_norm.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.06085456 -0.03316676  0.0039721 ]\n",
      "  stds:  [ 0.95486391  0.95334093  0.98921508]\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "for t in xrange(50):\n",
    "  X = np.random.randn(N, D1)\n",
    "  a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "  batchnorm_forward(a, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print 'After batch normalization (test-time):'\n",
    "print '  means: ', a_norm.mean(axis=0)\n",
    "print '  stds: ', a_norm.std(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.49363259555e-08\n",
      "dgamma error:  5.16687352013e-12\n",
      "dbeta error:  3.36435358982e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dgamma error: ', rel_error(da_num, dgamma)\n",
    "print 'dbeta error: ', rel_error(db_num, dbeta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Normalization: alternative backward\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For the sigmoid function, it turns out that you can derive a very simple formula for the backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can also derive a simple expression for the batch normalization backward pass if you work out derivatives on paper and simplify. After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster.\n",
    "\n",
    "NOTE: You can still complete the rest of the assignment if you don't figure this part out, so don't worry too much if you can't get it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx difference:  1.0\n",
      "dgamma difference:  0.943987880153\n",
      "dbeta difference:  0.0\n",
      "speedup: 1.26x\n"
     ]
    }
   ],
   "source": [
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print 'dx difference: ', rel_error(dx1, dx2)\n",
    "print 'dgamma difference: ', rel_error(dgamma1, dgamma2)\n",
    "print 'dbeta difference: ', rel_error(dbeta1, dbeta2)\n",
    "print 'speedup: %.2fx' % ((t2 - t1) / (t3 - t2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs2312n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the flag `use_batchnorm` is `True` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.ones((3,)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "['b2', 'gamma1', 'gamma2', 'W3', 'b1', 'beta1', 'b3', 'beta2', 'W1', 'W2']\n",
      "Initial loss:  2.50038007084\n",
      "['beta1', 'gamma2', 'gamma1', 'b2', 'b1', 'W3', 'beta2', 'W1', 'b3', 'W2']\n",
      "W1 relative error: 3.03e-06\n",
      "W2 relative error: 4.84e-06\n",
      "W3 relative error: 4.98e-09\n",
      "b1 relative error: 2.22e-08\n",
      "b2 relative error: 1.11e-08\n",
      "b3 relative error: 9.90e-11\n",
      "beta1 relative error: 1.09e-08\n",
      "beta2 relative error: 7.18e-09\n",
      "gamma1 relative error: 1.07e-08\n",
      "gamma2 relative error: 1.54e-08\n",
      "\n",
      "Running check with reg =  3.14\n",
      "['b2', 'gamma1', 'gamma2', 'W3', 'b1', 'beta1', 'b3', 'beta2', 'W1', 'W2']\n",
      "Initial loss:  7.29163911911\n",
      "['beta1', 'gamma2', 'gamma1', 'b2', 'b1', 'W3', 'beta2', 'W1', 'b3', 'W2']\n",
      "W1 relative error: 1.44e-07\n",
      "W2 relative error: 3.62e-06\n",
      "W3 relative error: 1.20e-07\n",
      "b1 relative error: 5.55e-09\n",
      "b2 relative error: 2.22e-08\n",
      "b3 relative error: 1.96e-10\n",
      "beta1 relative error: 8.30e-09\n",
      "beta2 relative error: 4.95e-08\n",
      "gamma1 relative error: 6.10e-09\n",
      "gamma2 relative error: 5.31e-08\n"
     ]
    }
   ],
   "source": [
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print 'Running check with reg = ', reg\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            use_batchnorm=True)\n",
    "\n",
    "  print model.params.keys()\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print 'Initial loss: ', loss\n",
    "  print grads.keys()\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))\n",
    "  if reg == 0: print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 200) loss: 2.308727\n",
      "(Epoch 0 / 10) train acc: 0.146000; val_acc: 0.129000\n",
      "(Epoch 1 / 10) train acc: 0.329000; val_acc: 0.267000\n",
      "(Epoch 2 / 10) train acc: 0.400000; val_acc: 0.320000\n",
      "(Epoch 3 / 10) train acc: 0.456000; val_acc: 0.343000\n",
      "(Epoch 4 / 10) train acc: 0.519000; val_acc: 0.291000\n",
      "(Epoch 5 / 10) train acc: 0.595000; val_acc: 0.326000\n",
      "(Epoch 6 / 10) train acc: 0.695000; val_acc: 0.342000\n",
      "(Epoch 7 / 10) train acc: 0.668000; val_acc: 0.338000\n",
      "(Epoch 8 / 10) train acc: 0.706000; val_acc: 0.318000\n",
      "(Epoch 9 / 10) train acc: 0.763000; val_acc: 0.327000\n",
      "(Epoch 10 / 10) train acc: 0.819000; val_acc: 0.327000\n",
      "(Iteration 1 / 200) loss: 2.302858\n",
      "(Epoch 0 / 10) train acc: 0.126000; val_acc: 0.132000\n",
      "(Epoch 1 / 10) train acc: 0.269000; val_acc: 0.246000\n",
      "(Epoch 2 / 10) train acc: 0.317000; val_acc: 0.277000\n",
      "(Epoch 3 / 10) train acc: 0.333000; val_acc: 0.258000\n",
      "(Epoch 4 / 10) train acc: 0.399000; val_acc: 0.298000\n",
      "(Epoch 5 / 10) train acc: 0.467000; val_acc: 0.322000\n",
      "(Epoch 6 / 10) train acc: 0.467000; val_acc: 0.303000\n",
      "(Epoch 7 / 10) train acc: 0.525000; val_acc: 0.327000\n",
      "(Epoch 8 / 10) train acc: 0.487000; val_acc: 0.290000\n",
      "(Epoch 9 / 10) train acc: 0.610000; val_acc: 0.324000\n",
      "(Epoch 10 / 10) train acc: 0.622000; val_acc: 0.323000\n"
     ]
    }
   ],
   "source": [
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "bn_solver.train()\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0lNHMBoMKZ2l9LrKtiZiP5z8IZaJEREQUfAzqfKBx1d8mgju1oKT4j8XA5UlP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\nQ50kSZIkZbH/BzQ3hYTEXtmPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f13c5c05250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o', label='baseline')\n",
    "plt.plot(bn_solver.loss_history, 'o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.train_acc_history, '-o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(solver.val_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.val_acc_history, '-o', label='batchnorm')\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "cs231n/layers.py:635: RuntimeWarning: divide by zero encountered in log\n",
      "  loss = -np.sum(np.log(probs[np.arange(N), y])) / N\n"
     ]
    }
   ],
   "source": [
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers = {}\n",
    "solvers = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "  print 'Running weight scale %d / %d' % (i + 1, len(weight_scales))\n",
    "  bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "  model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "  bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  bn_solver.train()\n",
    "  bn_solvers[weight_scale] = bn_solver\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  solver.train()\n",
    "  solvers[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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ppYKAJm1KKaWUUkFAkzallFJKqSCgSZtSSimlVBAo4HQASiml1NXExccxasookk0yhaUw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\nJZ8t8X9AHthzcg9NJzRlVuw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Rx3EFlIblGlLvTL3LEjaAA00P6FAYSqmQ9+uvUKwYVKzo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JExprWjwTgQgyZtSqksKfTpUKKKRd1wvP6e+iz8fqEFEbnOuXNw991w9Kht43OVcfvP7Kf5lOZU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\nAr52NBCVtf2b9G+mj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/iw/oe0e6CdV64fZoxh5s6Z9J3flzr31OGzxp/xvzz/c2udp+NP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\nKwBO2szMzMwKwP8DYHK+YCwa7HMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f13c1315d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "  best_train_accs.append(max(solvers[ws].train_acc_history))\n",
    "  bn_best_train_accs.append(max(bn_solvers[ws].train_acc_history))\n",
    "  \n",
    "  best_val_accs.append(max(solvers[ws].val_acc_history))\n",
    "  bn_best_val_accs.append(max(bn_solvers[ws].val_acc_history))\n",
    "  \n",
    "  final_train_loss.append(np.mean(solvers[ws].loss_history[-100:]))\n",
    "  bn_final_train_loss.append(np.mean(bn_solvers[ws].loss_history[-100:]))\n",
    "  \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.gcf().set_size_inches(10, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question:\n",
    "Describe the results of this experiment, and try to give a reason why the experiment gave the results that it did."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Answer:\n",
    "BN can nicely reduce the impact"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
